AI Vision Robot Tank Kit with LiDAR and Python Programming Available in Black and Green Why this kit matters Combine vision and LiDAR sensing to build practical autonomous robots and handson learning projects. Pythonbased control and example code let you focus on algorithms and behavior instead of lowlevel setup. Tracked chassis provides stable movement across varied indoor surfaces for repeatable testing and demonstrations. Key benefits Rapid prototyping: Integrated vision and LiDAR allow testing of navigation, obstacle avoidance, mapping, and object detection with minimal assembly. Educational value: Teaches realworld robotics concepts including sensor fusion, computer vision, and control using a single, Pythonfriendly platform. Flexible development: Modular design supports adding sensors and actuators so projects can grow from simple experiments to advanced prototypes. Core functions and capabilities Vision system for camerabased tasks such as object recognition, tracking, and visual feedback for autonomous behaviors. LiDARbased distance sensing for accurate obstacle detection, shortrange mapping, and improved path planning. Python programming interface with sample code and libraries for rapid development of algorithms and applications. Tracked drive train for improved traction and stability during maneuvers. Compatibility and integration Designed for use with controllers and singleboard computers that support Python and standard serial or USB interfaces. Works with common robotics and computer vision software libraries for straightforward integration of perception and control. Modular connectors and mounting points simplify adding external sensors, servos, and communication modules. Form factor and build Compact tracked tank chassis suitable for tabletop and indoor testing. Durable construction intended to support routine development and classroom use. Available in two color options: Black and Green. Performance considerations Integrated sensor suite enables reliable shortrange obstacle detection and visionenabled tasks. Tracked configuration offers consistent traction on tile, wood, carpet, and other indoor surfaces. Performance depends on the choice of controller, processing hardware, and software optimizations selected by the user. Typical use scenarios University or research lab: Prototype autonomous navigation algorithms and sensor fusion approaches using a combined LiDAR and camera platform. STEM classroom or workshop: Teach students Python programming, robotics fundamentals, and handson electronics with guided exercises and sample projects. Home developer or hobbyist: Build a mobile mapper, indoor inspection rover, or visionguided robot and iterate quickly using Python and available libraries. What sets it apart Combines LiDAR distance sensing and camera vision in a single, Pythonaccessible kit to accelerate development of perceptiondriven robotics projects. Focused on practical learning and prototyping without requiring deep firmware development before getting started. Package overview Trackbased robot chassis with integrated vision and LiDAR sensing and a Pythonready software interface. Color options: Black and Green. This kit is intended for developers, educators, and hobbyists who need a compact, extensible platform for learning and prototyping robotic perception and autonomous behaviors.